D465 Data Applications
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Free D465 Data Applications Questions
What is the primary focus of Big Data in the context of data science?
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Creating visual representations of data
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Handling and analyzing large volumes of data
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Developing machine learning algorithms
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Performing statistical analysis on small data sets
Explanation
Explanation:
The primary focus of Big Data in data science is to handle, store, and analyze extremely large and complex datasets that traditional data processing tools cannot manage efficiently. Big Data technologies and frameworks, such as Hadoop and Spark, enable data scientists to process vast amounts of structured and unstructured data from diverse sources like social media, sensors, and transactions. The goal is to uncover hidden patterns, correlations, and insights that can drive strategic decision-making and innovation. Big Data emphasizes the “three Vs”: volume, velocity, and variety, representing the size, speed, and diversity of the data being analyzed.
Correct Answer:
Handling and analyzing large volumes of data
A data analyst is working with a data frame named salary_data. They want to create a new column named wages that includes data from the rate column multiplied by 40. What code chunk lets the analyst create the wages column?
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mutate(salary_data, wages = rate * 40)
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mutate(salary_data, wages = rate + 40)
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mutate(salary_data, rate = wages * 40)
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mutate(wages = rate * 40)
Explanation
Explanation:
The mutate() function from the dplyr package is used to create new columns or modify existing ones in a data frame. To create a wages column that is calculated as the rate column multiplied by 40, the correct syntax specifies the data frame first (salary_data), then uses wages = rate * 40 inside the function. This ensures that the calculation is applied row-wise to the data frame and the new column is added correctly.
Correct Answer:
mutate(salary_data, wages = rate * 40)
Explain how deep learning differs from traditional machine learning techniques in terms of data processing and model complexity.
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Deep learning requires less data and simpler models
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Deep learning utilizes neural networks to automatically extract features from raw data, allowing for more complex models
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Deep learning is only applicable to image processing tasks.
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Deep learning relies on manual feature extraction and simpler algorithms.
Explanation
Explanation:
Deep learning differs from traditional machine learning by using multi-layered neural networks that can automatically learn and extract features from raw data. This allows for handling more complex patterns and relationships without extensive manual feature engineering. Deep learning models are generally more complex and require larger datasets compared to traditional machine learning, which often relies on simpler algorithms and manual feature extraction. While deep learning excels at image and speech processing, it is not limited to these domains.
Correct Answer:
Deep learning utilizes neural networks to automatically extract features from raw data, allowing for more complex models.
What is the primary function of prescriptive analytics in data science?
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To analyze historical data
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To recommend actions for desired outcomes
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To visualize data trends
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To clean and transform raw data
Explanation
Explanation:
Prescriptive analytics goes beyond descriptive and predictive analytics by recommending specific actions to achieve desired outcomes. It uses data, models, and algorithms to suggest the best course of action for decision-making. While analyzing historical data and visualizing trends are important, prescriptive analytics specifically focuses on providing actionable recommendations rather than just insights or forecasts.
Correct Answer:
To recommend actions for desired outcomes
Which of the following is NOT a key application of data science in transportation?
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Route optimization
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Traffic prediction
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Autonomous vehicles
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Market basket analysis
Explanation
Explanation:
In transportation, data science applications include route optimization, traffic prediction, and the development of autonomous vehicles, all of which enhance efficiency, safety, and decision-making in transport systems. Market basket analysis, by contrast, is a retail-focused technique used to analyze consumer purchasing patterns and is not relevant to transportation analytics.
Correct Answer:
Market basket analysis
What is the primary focus of Natural Language Processing (NLP) in the context of data science?
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Analyzing and modeling human language
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Visualizing data trends
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Predicting future outcomes
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Cleaning and transforming data
Explanation
Explanation:
In data science, Natural Language Processing (NLP) focuses on enabling computers to analyze and model human language in a way that allows meaningful interaction and interpretation. NLP techniques are designed to process unstructured text data—such as documents, emails, or social media posts—to extract insights, detect sentiment, and recognize entities or intent. It combines computational linguistics, machine learning, and deep learning to understand the structure, grammar, and semantics of language. This makes it a vital tool for applications such as chatbots, text summarization, speech recognition, and translation.
Correct Answer:
Analyzing and modeling human language
Explain how data science can be utilized for injury prediction in sports. What types of data might be analyzed?
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By analyzing player performance metrics and historical injury data
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By monitoring fan engagement on social media
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By evaluating ticket sales trends
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By assessing weather conditions during games
Explanation
Explanation:
Data science can be used to predict sports injuries by analyzing detailed datasets, including player performance metrics, training loads, biometrics, and historical injury records. Machine learning models can identify patterns or risk factors that increase the likelihood of injury, enabling coaches and medical staff to implement preventive measures. This proactive approach helps optimize player health and team performance, unlike fan engagement or ticket sales data, which do not provide insights into injury risk.
Correct Answer:
By analyzing player performance metrics and historical injury data
____ code is code that can be inserted directly into a .rmd file.
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Executable
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YAML
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Markdown
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Inline
Explanation
Explanation:
Inline code in R Markdown refers to code that can be written directly within the text of a .rmd file and is executed when the document is rendered. Inline code is typically surrounded by single backticks with an r (e.g., `r 2 + 2`), allowing the result of the code to appear directly in the output. This is useful for dynamically displaying values, calculations, or results without creating a separate code chunk.
Correct Answer:
Inline
An analyst is organizing a dataset in RStudio using the following code:
arrange(filter(Storage_1, inventory >= 40), count)
Which of the following examples is a nested function in the code?
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filter
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count
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arrange
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inventory
Explanation
Explanation:
In the provided code, the filter() function is nested inside the arrange() function. A nested function is one that is called inside another function. Here, filter(Storage_1, inventory >= 40) first filters the dataset for rows where inventory is greater than or equal to 40, and then the arrange() function organizes the filtered data by the count column. Nesting functions allows analysts to perform multiple operations in a single line of code efficiently.
Correct Answer:
filter
A data analyst wants a high-level summary of the structure of their data frame, including the column names, the number of rows and variables, and the type of data within a given column. What function should they use?
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rename_with()
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head()
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str()
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colnames()
Explanation
Explanation:
The str() function in R provides a concise overview of a data frame’s structure, showing the number of rows, the number of columns, column names, and the type of data in each column. This function is essential for quickly understanding the dataset before performing further analysis, as it allows analysts to verify data types, check for missing values, and get a snapshot of the data without displaying the entire dataset like head() does.
Correct Answer:
str()
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